Elderly fall detection in Parkinson’s disease (PD) and epilepsy can cause broken bones or other injuries, decreasing the quality of life and possibly resulting in death. However, such fall-detection systems suffer from certain limitations and challenges such as fall-detection accuracy. This work aims to design and implement a wearable fall-detection system (WFDS) for PD patients based on the low-power ZigBee wireless sensor network (WSN). Patient falls were accurately detected based on the data event algorithm (DEA) results of two wireless sensor nodes an accelerometer and Myoware mounted on the patient’s body. The fall direction of the PD was accurately determined based on a direction fall event (DFE) algorithm in the receiver node. The experimental results show that the WFDS achieved 100% accuracy, sensitivity, and specificity in detecting the patient’s fall. The experimental WFDS appears to outperform existing modalities in terms of fall detection sensitivity, accuracy, and specificity.